{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用了具有LayNormal的FC网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任务3：\n",
    "输入1（cycle）2（EMG_retus_femoris_l=emg_rf_l）3（EMG_lat_hams_l=emg_lh_l），输出 7（LKneeAngle = ka_l）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据不进行分类，抽取4个作测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from visdom import Visdom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import torch.nn.init as init\n",
    "from torchsummary import summary\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import datetime\n",
    "#import tool\n",
    "from tool import *\n",
    "import torch.nn.functional as F\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n"
     ]
    }
   ],
   "source": [
    "# 可视化\n",
    "timeForSave = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n",
    "path = 'G:\\科研学习\\肌电信号\\Code\\musle force\\\\'\n",
    "#path ='/mnt/data/ZP_Project/Muscle/muscle/'\n",
    "Visdom_Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Visdom\\\\'\n",
    "if not os.path.exists(Visdom_Dir):\n",
    "    os.makedirs(Visdom_Dir)\n",
    "image_Dir =path+'image_Mission3_FC_IN_all_together'\n",
    "if not os.path.exists(image_Dir):\n",
    "        os.makedirs(image_Dir)\n",
    "\n",
    "\n",
    "class visdom_account:\n",
    "    def __init__(self):\n",
    "        self.port = 8097\n",
    "        self.server = \"http://localhost\"\n",
    "        self.base_url = \"/\"\n",
    "        self.username = \"admin\"\n",
    "        self.passward = \"1234\"\n",
    "        self.evns = \"Muscle\"\n",
    "\n",
    "\n",
    "viz_acnt = visdom_account()\n",
    "\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz.delete_env('Muscle')  # 删除环境\n",
    "\n",
    "# viz.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#          opts={'title': 'ProcessMonitor', },)\n",
    "# viz1.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#           opts={'title': 'ProcessMonitor', },)\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz1 = Visdom(env=viz_acnt.evns,\n",
    "              log_to_filename=Visdom_Dir+'vislog_'+timeForSave)\n",
    "viz_batch = []\n",
    "for i in range(32):\n",
    "    viz_batch.append(Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed_redacted_GIL01_Fast5.mat\n",
      "Transformed_redacted_GIL01_Free4.mat\n",
      "Transformed_redacted_GIL01_slow3.mat\n",
      "Transformed_redacted_GIL01_XSlow2.mat\n",
      "Transformed_redacted_GIL02_Fast3.mat\n",
      "Transformed_redacted_GIL02_Free2.mat\n",
      "Transformed_redacted_GIL02_slow1.mat\n",
      "Transformed_redacted_GIL02_XSlow2.mat\n",
      "Transformed_redacted_GIL03_Free4.mat\n",
      "Transformed_redacted_GIL03_slow3.mat\n",
      "Transformed_redacted_GIL03_xFast2.mat\n",
      "Transformed_redacted_GIL03_XSlow1.mat\n",
      "Transformed_redacted_GIL04_Fast2.mat\n",
      "Transformed_redacted_GIL04_Free3.mat\n",
      "Transformed_redacted_GIL04_slow3.mat\n",
      "Transformed_redacted_GIL04_XSlow1.mat\n",
      "Transformed_redacted_GIL06_Fast3.mat\n",
      "Transformed_redacted_GIL06_Free2.mat\n",
      "Transformed_redacted_GIL06_slow2.mat\n",
      "Transformed_redacted_GIL06_XSlow1.mat\n",
      "Transformed_redacted_GIL08_Fast1.mat\n",
      "Transformed_redacted_GIL08_Free1.mat\n",
      "Transformed_redacted_GIL08_slow4.mat\n",
      "Transformed_redacted_GIL08_XSlow2.mat\n",
      "Transformed_redacted_GIL11_Fast2.mat\n",
      "Transformed_redacted_GIL11_Free1.mat\n",
      "Transformed_redacted_GIL11_slow2.mat\n",
      "Transformed_redacted_GIL11_XSlow1.mat\n",
      "Transformed_redacted_GIL12_Fast2.mat\n",
      "Transformed_redacted_GIL12_Free4.mat\n",
      "Transformed_redacted_GIL12_slow4.mat\n",
      "Transformed_redacted_GIL12_XSlow1.mat\n"
     ]
    }
   ],
   "source": [
    "#读取并合并\n",
    "#尝试直接把数据相加\n",
    "Dir = path + 'Transed_Data/'\n",
    "filenames = os.listdir(Dir)\n",
    "# Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data\\\\'\n",
    "# filenames = os.listdir('G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data')\n",
    "\n",
    "data_rf = []\n",
    "data_lh = []\n",
    "data_kal = []\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = scio.loadmat(Dir + str(filename))\n",
    "    #print(data.keys())  #dict_keys(['__header__', '__version__', '__globals__', 'cyc', 'emg_lh_l', 'emg_rf_l', 'ka_l', 'mf_bm_l', 'mf_rf_l'])\n",
    "    data1 = data['emg_rf_l']\n",
    "    #data1 = data1.squeeze()\n",
    "    data2 = data['emg_lh_l']\n",
    "    data3 =  data['ka_l']\n",
    "    #data2 = data2.squeeze()\n",
    "    data_rf.append(data1)\n",
    "    data_lh.append(data2)\n",
    "    data_kal.append(data3)\n",
    "\n",
    "data_rf = np.array(data_rf)\n",
    "data_lh = np.array(data_lh)\n",
    "data_kal = np.array(data_kal)\n",
    "set=MuscleData2(data_rf,data_lh,data_kal)\n",
    "train_set,test_set =  torch.utils.data.dataset.random_split(set, [28, 4])\n",
    "    # sampel = next(iter(set))\n",
    "    # print(sampel,'\\nnnnnnn')\n",
    "\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(set,[28,4])\n",
    "\n",
    "# train_loader =torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, pin_memory=False)\n",
    "# test_loader =torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, pin_memory=False)\n",
    "# train_test_loader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"此代码块为Net训练\"\"\"\n",
    "\n",
    "Net = FC_IN_Net2()\n",
    "Net.apply(weights_init)\n",
    "Net.to(device)\n",
    "\n",
    "train_loader=torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, pin_memory=False)\n",
    "test_loader=torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=False)\n",
    "\n",
    "\n",
    "criterion = torch.nn.MSELoss()\n",
    "epoch_num = 8000\n",
    "\n",
    "optimizer = torch.optim.SGD(Net.parameters(),\n",
    "lr=0.0000015,momentum=0.9)\n",
    "vizx = 0\n",
    "#开始训练\n",
    "for epoch in range(epoch_num):\n",
    "        #n = 0   #此n用于visdom画图。每个epoch对每个样本进行画图\n",
    "        for batch in train_loader:\n",
    "                vizx+=1\n",
    "                data,data2,label = batch\n",
    "                data = torch.as_tensor(data)\n",
    "                data2 = torch.as_tensor(data2)\n",
    "                data = data.type(torch.FloatTensor)\n",
    "                data2 = data2.type(torch.FloatTensor)\n",
    "                \n",
    "                #print(data.shape)       #torch.Size([1, 101, 1])\n",
    "                label =torch.as_tensor(label)\n",
    "                label = label.squeeze(2)\n",
    "                #print(label.shape)\n",
    "                label = label.type(torch.FloatTensor)\n",
    "                data = data.cuda()\n",
    "                data2 = data2.cuda()\n",
    "                label = label.cuda()\n",
    "                #print(data.shape)\n",
    "                pred = Net(data,data2)\n",
    "                #print(pred.shape)\n",
    "                #print(label.shape)\n",
    "                #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                #print('pred',pred.shape)\n",
    "                #print('label',label.shape)     #label torch.Size([101, 1])\n",
    "                loss = criterion(pred,label)\n",
    "                \n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                #viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\n",
    "                viz_batch[0].line([float(loss)],[vizx],win='loss'+str(0), name='loss'+str(0), update='append',opts=dict(title='train_loss'))\n",
    "                optimizer.step()\n",
    "                #n +=1\n",
    "                #print(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i =0\n",
    "#测试\n",
    "for batch in test_loader:\n",
    "        data1,data2,label = batch\n",
    "        data1 = torch.as_tensor(data1)\n",
    "        data2 = torch.as_tensor(data2)\n",
    "        data1 = data1.type(torch.FloatTensor)\n",
    "        data2 = data2.type(torch.FloatTensor)\n",
    "        data1 = data1.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        data2 = data2.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        # label =label.to(device)\n",
    "        #data1 = data1.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "        pred = Net(data1,data2)\n",
    "        #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "        pred = pred.cpu()\n",
    "        pred = pred.data.numpy()\n",
    "        pred=np.squeeze(pred)\n",
    "        label=np.squeeze(label)\n",
    "        data1 =data1.cpu()\n",
    "        data2 =data2.cpu()\n",
    "        data1 = np.squeeze(data1)\n",
    "        data2 = np.squeeze(data2)\n",
    "        #利用plot画图\n",
    "        plt.figure(i)\n",
    "        plt.title('test_sample'+str(i))\n",
    "        plt.plot(pred, label='pred')\n",
    "        plt.plot(label, color='red', label='GroundTruth')\n",
    "        plt.plot(data1*100, color='black', label='data1')\n",
    "        plt.plot(data2*100, color='black', label='data2')\n",
    "        plt.legend()\n",
    "        #增\n",
    "        figname =image_Dir+'/imag_ {}.jpg'.format(i)\n",
    "        plt.savefig(figname)\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "        i +=1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #用于打印网络里权重。默认注释掉\n",
    "# Net.state_dict().keys()\n",
    "# for i in range(len(test_loader)):\n",
    "#     print(i)\n",
    "#     # print(Net[i].fc1.weight)\n",
    "#     # print(Net[i].fc2.weight)\n",
    "#     # print(Net[i].out.weight)\n",
    "#     w1 =Net[i].fc1.weight.cpu().data.numpy()\n",
    "#     w2 =Net[i].fc2.weight.cpu().data.numpy()\n",
    "#     w3 =Net[i].out.weight.cpu().data.numpy()\n",
    "#     w1 = np.squeeze(w1)\n",
    "#     w2 = np.squeeze(w1)\n",
    "#     w3 = np.squeeze(w1)\n",
    "#     #ss\n",
    "#     plt.figure(i)\n",
    "#     plt.title('Net'+str(i))\n",
    "#     plt.plot(w1, label='fc1.weight')\n",
    "#     plt.plot(w2, color='red', label='fc2.weight')\n",
    "#     plt.plot(w3, color='black', label='out.weight')\n",
    "#     plt.legend()\n",
    "#     #figname ='./image_Mission1_FC/imag_ {}.jpg'.format(i)\n",
    "#     #plt.savefig(figname)\n",
    "#     plt.show()\n",
    "#     plt.close()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e3b95d8d6e207246d09e19daee9bbc5bbe4502ff924268508716ea3969933742"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('pytorch')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
